函数作用
各位看官老爷，如果觉得对您有用麻烦赏个子，创作不易，0.1元就行了。下面是微信乞讨码：
>>> input2=torch.randn(1,4,2,2,3) #输入维度为5维（b，c，l，h，w）
>>> nnnn=nn.GroupNorm(2,4) #定义组规范化
>>> output1=nnnn(input2)
>>> output1
tensor([[[[[-0.7846, 0.0791, 1.5367],
[ 0.3729, -0.6258, -0.4306]],
[[-0.0724, 0.9401, -0.3869],
[-1.1452, 1.1260, 2.3547]]],
[[[ 1.3363, 1.1639, -1.5671],
[-0.1317, 0.1545, -0.5496]],
[[-1.8654, 0.1424, -0.3734],
[ 0.2217, -0.3059, -1.1897]]],
[[[-0.9365, -0.8634, 2.1091],
[-0.2412, -2.1942, 1.1618]],
[[-0.0543, 0.3332, -1.3826],
[-0.6508, 0.7949, -0.6618]]],
[[[ 1.4304, 0.8079, -0.6333],
[ 0.9667, -0.7094, 0.2172]],
[[-0.3731, -0.4647, 1.4705],
[ 0.7568, -0.1945, -0.6886]]]]], grad_fn=<AddcmulBackward>)
>>> output1.size() #输出的size和输入相同
torch.Size([1, 4, 2, 2, 3])
>>>input2
tensor([[[[[-0.3088, 0.6669, 2.3133],
[ 0.9987, -0.1293, 0.0911]],
[[ 0.4958, 1.6394, 0.1405],
[-0.7161, 1.8495, 3.2374]]],
[[[ 2.0870, 1.8923, -1.1927],
[ 0.4288, 0.7520, -0.0433]],
[[-1.5297, 0.7383, 0.1557],
[ 0.8279, 0.2320, -0.7664]]],
[[[-1.1690, -1.0956, 1.8887],
[-0.4709, -2.4317, 0.9377]],
[[-0.2833, 0.1058, -1.6169],
[-0.8822, 0.5693, -0.8932]]],
[[[ 1.2074, 0.5824, -0.8645],
[ 0.7418, -0.9409, -0.0107]],
[[-0.6034, -0.6953, 1.2476],
[ 0.5311, -0.4240, -0.9201]]]]])
>>> input2[0,0:2].mean()
tensor(0.5775)
>>> input2[0,0:2].var()
tensor(1.3314)
>>>nnn.weight
Parameter containing:
tensor([1., 1., 1., 1.], requires_grad=True)
>>> nnnn.bias
Parameter containing:
tensor([0., 0., 0., 0.], requires_grad=True)
>>> bb=nn.GroupNorm(2,4,affine=True) #查看参数
>>> bb.weight
Parameter containing:
tensor([1., 1., 1., 1.], requires_grad=True)
>>> bb.bias
Parameter containing:
tensor([0., 0., 0., 0.], requires_grad=True)
